Statistical Models for Multidisciplinary Applications of Image Segmentation and Labelling

Jan Cornelis, Edgard Nyssen, Antonis Katartzis, Luc Van Kempen, Piet Boekaerts, Rudi Deklerck, Alexandru Salomie Ioan

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

Three classes of statistical techniques used to solve image segmentation and labelling problems are reviewed: (1) supervised and unsupervised pixel classification, (2) exploitation of the probability distribution map as a way to model image structure, (3) Markov random field modelling combined with MAP statistical classification. Diverse examples illustrate the potential of the three approaches that are described as generic methods belonging to a common framework for image segmentation/labelling
Original languageEnglish
Title of host publicationWCC 2000 - ICSP2000, World Computer Conference 2000 - 5th International Conference on Signal Processing Proceedings; Beijing, China III/III ; Aug 2000.
EditorsYuan Baozong, Tang Xiaofang
Publisher16 th World Computer Conference 2000 (WCC 2000) - 5th International Conference on Signal Processing (ICSP2000) Proceedings, Vol. III, pp. 2103-2110, Beijing, China.
Pages2103-2110
Number of pages8
VolumeIII
Publication statusPublished - Aug 2000

Bibliographical note

16 th World Computer Conference 2000 (WCC 2000) - 5th International Conference on Signal Processing (ICSP2000) Proceedings, Vol. III, pp. 2103-2110, Beijing, China.
Series editor: Yuan Baozong, Tang Xiaofang

Fingerprint

Dive into the research topics of 'Statistical Models for Multidisciplinary Applications of Image Segmentation and Labelling'. Together they form a unique fingerprint.

Cite this